Software solutions
with such an approach, including artifacts in the production that are not defects, differing sensitivities to defects in different areas and the fact that defect definition is dynamic. Ideally, a model design for defect detection should represent the knowledge and understanding of the quality manager. They know their product better than anyone else and their input and feedback can mitigate many of the problems described above. The optimal solution is therefore a model that is closer to the unsupervised end of the spectrum, but without the drawbacks of the fully unsupervised system. Lean AI’s unsupervised system is designed with this goal in mind. Rather than having to tag lots of data yourself, you can simply feed the model untagged data and it learns for itself, unsupervised, what a defective product looks like. There is no getting away from the reality of feeding it a lot of images, but this process is automated and therefore quicker and easier. An unsupervised model can automate the process of building the model because its algorithms allow it to stream untagged images and work out for itself what possible defects look like. However, once it identifies outliers or potential defects, you need someone with knowledge of the product to provide that feedback and allow the model to continually optimize. With this approach you leverage the knowledge of the quality manager, but you do not wear them out by requiring labelling thousands of images.
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How long does this process take? Here is the big return on investment. Compared to the conventional model which takes months to be ready to deploy to the production line, the unsupervised model can deliver a workable solution in few weeks or less. The model itself can do the learning on the production line, saving you time and hassle. And with the input of the quality manager, you enjoy the benefits of automation without the problems encountered with fully unsupervised systems which has so far failed to deliver a workable solution.
The best of both worlds is an AI solution that allows the quality manager to retain control over what the AI system learns, but avoids the hassle of having to waste months of work tagging. Lean AI’s unsupervised system is designed to deliver this vision, leveraging and integrating the quality control knowledge you have, but automating the tedious work that is required for the supervised model.
Lean AI
lean-ai-tech.com Instrumentation Monthly May 2023
SMART SOFTWARE AND ACTIONABLE INTELLIGENCE
n the fast, cost- effective, sustainable future, smart software is paving the way to optimised production and improved decision
making. Digital technology and smart software have already transformed the machining industry with unprecedented access to actionable data for better results in less time. With a wealth of data generated from production processes of all kinds, shops need to understand how to use that data to their advantage. Now, progressive
manufacturers can move toward even greater benefits with software that fulfills the promises of Industry 4.0.
QUICK ACCESS TO PRODUCT DATA Past years have shown the machining industry that sustainable cost containment holds the key to stability and survival, even in turbulent economic times. At the same time, the industry realised it needed faster ways to develop new technologies and tools that can respond to changing circumstances. Smart software is a key to making these developments responsive and effective. In some situations, it can enable manufacturers to optimise machining and production processes by up to 40 per cent, eliminating some of the repetitive manual processes. Non-digitised processes force production personnel to look up product information manually, which wastes time and may not yield accurate results. For example, to reduce routine tasks on the shop floor, with the help of Seco Assistant smartphone app, production personnel can simply scan the product package or the tool. As a result, they can quickly receive relevant product information or calculate cutting data and compare insert geometries and grades from different suppliers.
SMART WAYS TO MANAGE INVENTORIES
Thirty to 60 per cent of tooling inventory is likely to be uncontrolled, floating around the shop floor or simply stacked by machines in excess quantities. Smart software, such as the Inventory Management system, can help
to tackle this issue in a more cost-effective and secure way.
Apart from physical flexibility, its main benefit lies in monitoring tool and equipment usage and increasing staff accountability. This way, the inventory management systems help to reduce wasted set-up time because of misplaced items and keep track of high-value tools and mission critical items.
LESS HUMAN INVOLVEMENT IN CAM PROGRAMMING
In future, smart machining technologies are likely to allow the feature recognition in components, build quick CAM programming and supply NC codes to machines, saving up to 80 per cent of time in the engineering preparation area. Nevertheless, due to the high complexity of some components such as turbine blades and structural parts, human intelligence and technical expertise are still required. Engineering Services from Seco can help with optimisation of new or existing complex machining processes and identifying gaps in CAM programming.
“In a factory network, smart software can eliminate unnecessary inventory of materials and tooling for up to a 20 per cent reduction in inventory costs,” says Janardhan N., engineering services solution manager at Seco Tools. “With processes optimised for efficiency through technology that removes repetitive manual processes from the production line, labour productivity can rise up to 30 per cent and machine downtime drop by up to 50 per cent.”
Seco Tools
www.secotools.com 65
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